Zachary Depp, Halit Bugra Tulay, C. Emre Koksal (The Ohio State University)

The traditional vehicular roll-jam attack is an effective means to gain access to the target vehicle by jamming and recording key fob inputs from a victim. However, it requires specific knowledge of the attack surface, and delicate tuning of software-defined radio parameters. We have developed an enhanced version of the roll-jam attack that uses a known noise signal for jamming, in contrast to the additive white Gaussian noise that is typically used in the attack. Using a known noise signal allows for less strict tuning of the software-defined radios used in the attack, and allows for digital noise removal of the recorded input to enhance the replay attack.

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Smarter Contracts: Detecting Vulnerabilities in Smart Contracts with Deep...

Christoph Sendner (University of Wuerzburg), Huili Chen (University of California San Diego), Hossein Fereidooni (Technische Universität Darmstadt), Lukas Petzi (University of Wuerzburg), Jan König (University of Wuerzburg), Jasper Stang (University of Wuerzburg), Alexandra Dmitrienko (University of Wuerzburg), Ahmad-Reza Sadeghi (Technical University of Darmstadt), Farinaz Koushanfar (University of California San Diego)

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Location Spoofing Attacks on Autonomous Fleets

Jinghan Yang, Andew Estornell, Yevgeniy Vorobeychik (Washington University in St. Louis)

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Cyclops: Binding a Vehicle’s Digital Identity to its Physical...

Lewis William Koplon, Ameer Ghasem Nessaee, Alex Choi (University of Arizona, Tucson), Andres Mentoza (New Mexico State University, Las Cruces), Michael Villasana, Loukas Lazos, Ming Li (University of Arizona, Tucson)

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ReScan: A Middleware Framework for Realistic and Robust Black-box...

Kostas Drakonakis (FORTH), Sotiris Ioannidis (Technical University of Crete), Jason Polakis (University of Illinois at Chicago)

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